Instructions to use whaleloops/keptlongformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whaleloops/keptlongformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="whaleloops/keptlongformer")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("whaleloops/keptlongformer") model = AutoModelForMaskedLM.from_pretrained("whaleloops/keptlongformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 040c3ab36e2f220815157bb5a4da7c06ccc3348ca01a6a25974954e735a41c80
- Size of remote file:
- 633 MB
- SHA256:
- 91f7209e1883b5ef2c774bd97ca7a64dfde03f685b26c7d2ebe02a5d4905d85a
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